scholarly journals Screening and discrimination of optimal prognostic genes for pancreatic cancer based on a prognostic prediction model

2021 ◽  
Author(s):  
Zhiqin Chen ◽  
Haifei Song ◽  
Xiaochen Zeng ◽  
Ming Quan ◽  
Yong Gao
Keyword(s):  
Pancreatic Cancer ◽  
Prediction Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Molecular Therapy ◽  
Clinical Factors ◽  
Cox Regression Analysis ◽  
Gene Set ◽  
Prognosis Prediction ◽  
Prognosis Prediction Model

Abstract The prognosis of pancreatic cancer is poor because patients are usually asymptomatic in the early stage and the early diagnostic rate is low. Therefore, in this study, we aimed to identify potential prognosis-related genes in pancreatic cancer to improve diagnosis and the outcome of patients. The mRNA expression profile data from The Cancer Genome Atlas database and GSE79668, GSE62452, and GSE28735 datasets from Gene Expression Omnibus were downloaded. The prognosis-relevant genes and clinical factors were analyzed using Cox regression analysis and the optimal gene sets were screened using the Cox proportional model. Next, the Kaplan-Meier survival analysis was used to evaluate the relationship between risk grouping and patient prognosis. Finally, an optimal gene-based prognosis prediction model was constructed and validated using a test dataset to discriminate the model accuracy and reliability. The results showed that 325 expression variable genes were identified, and 48 prognosis-relevant genes and three clinical factors, including lymph node stage (pathologic N), new tumor, and targeted molecular therapy were preliminarily obtained. In addition, a gene set containing 16 optimal genes was identified and included FABP6, MAL, KIF19, and REG4, which were significantly associated with the prognosis of pancreatic cancer. Moreover, a prognosis prediction model was constructed and validated to be relatively accurate and reliable. In conclusion, a gene set consisting of 16 prognosis-related genes was identified and a prognosis prediction model was constructed, which is expected to be applicable in the clinical diagnosis and treatment guidance of pancreatic cancer in the future.

scholarly journals Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Xin Qiu ◽  
Qin-Han Hou ◽  
Qiu-Yue Shi ◽  
Hai-Xing Jiang ◽  
Shan-Yu Qin
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Pancreatic Cancer ◽  
Cancer Progression ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

scholarly journals PVT1 is a prognostic marker associated with immune invasion of bladder urothelial carcinoma

2021 ◽  
Vol 19 (1) ◽  
pp. 169-190
Author(s):  
Peiyuan Li ◽  
◽  
Gangjie Qiao ◽  
Jian Lu ◽  
Wenbin Ji ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set ◽  
Bladder Urothelial Carcinoma

<abstract> <p>Plasmacytoma variant translocation 1 (PVT1) is involved in multiple signaling pathways and plays an important regulatory role in a variety of malignant tumors. However, its role in the prognosis and immune invasion of bladder urothelial carcinoma (BLCA) remains unclear. This study investigated the expression of PVT1 in tumor tissue and its relationship with immune invasion, and determined its prognostic role in patients with BLCA. Patients were identified from the cancer genome atlas (TCGA). The enrichment pathway and function of PVT1 were explained by gene ontology (GO) term analysis, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA), and the degree of immune cell infiltration was quantified. Kaplan–Meier analysis and Cox regression were used to analyze the correlation between PVT1 and survival rate. PVT1-high BLCA patients had a lower 10-year disease-specific survival (DSS P &lt; 0.05) and overall survival (OS P &lt; 0.05). Multivariate Cox regression analysis showed that PVT1 (high vs. low) (P = 0.004) was an independent prognostic factor. A nomogram was used to predict the effect of PVT1 on the prognosis. PVT1 plays an important role in the progression and prognosis of BLCA and can be used as a medium biomarker to predict survival after cystectomy.</p> </abstract>

scholarly journals Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Chen Jin ◽  
Rui Li ◽  
Tuo Deng ◽  
Jialiang Li ◽  
Yan Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Prediction Ability ◽  
Cox Regression Analysis

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

scholarly journals Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Hao Zuo ◽  
Luojun Chen ◽  
Na Li ◽  
Qibin Song
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Transcriptional Level ◽  
Cox Regression Analysis

Pancreatic cancer is known as “the king of cancer,” and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

scholarly journals Systematic Analysis of the Functions and Prognostic Values of Rna Binding Protein in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Author(s):  
song jukun ◽  
Feng Liu ◽  
Bo Liu ◽  
Xianlin Cheng ◽  
Xinhai Yin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Head And Neck ◽  
Clinical Decision Making ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Clinical Decision ◽  
The Cancer Genome Atlas ◽  
Systematic Analysis ◽  
Cox Regression Analysis

Abstract Background: Dysregulation of RNA-binding proteins (RBPs) playsan important role in controlling processes in cancer development.However, the function of RBPs has not been thoroughly and systematically documented in head and neck cancer.We aim to explore the role of RPB in the pathogenesis of HNSC.Methods: We obtained HNSC gene expression data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and the GEO databases, andidentified aberrantly expressed RBPs between tumors and normal tissues.Meanwhile, we performed a series of bioinformatics to explore the function and prognostic value of these RBPs.Results: A total of 249 abnormally expressed RBPs were identified, including 101 down-regulated RBPs and 148 up-regulated RBPs.Using least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analysis, the fifteen RPBs were identified as hub genes. With the fifteen RPBS, the prognostic prediction model was constructed.Further analysis showed that the high-risk score of the patients expressed a better survival outcome. The prediction model was validated in another HNSC dataset, and similar findings were observed. Conclusions: Our results provide novel insights into the pathogenesis of HNSC. The fifteen RBP gene signature exhibited the predictive value of moderate HNSC prognosis, and have potential application value in clinical decision-making and individualized treatment.

scholarly journals Ferroptosis-Related Gene Signature and Patterns of Immune Infiltration Predict the Overall Survival in Patients With Lung Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Yuxuan Wang ◽  
Weikang Chen ◽  
Minqi Zhu ◽  
Lei Xian
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Prognosis Prediction

Background: Lung adenocarcinoma (LUAD) is a malignant tumor with high heterogeneity and poor prognosis. Ferroptosis, a form of regulated cell-death–related iron, has been proven to trigger inflammation-associated immunosuppression in the tumor microenvironment, which promotes tumor growth. Therefore, the clinical prognostic value of ferroptosis-related genes in LUAD needs to be further explored.Method: In this study, we downloaded the mRNA expression profiles and corresponding clinical data of LUAD patients from the Cancer Genome Atlas database. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct ferroptosis-related gene signature. Based on these, we established the nomograms for prognosis prediction and validated the model in the GSE72094 dataset. The cell type was identified using the CIBERSORT algorithm for estimating relative subsets of RNA transcripts, which was then used to screen significant tumor immune-infiltrating cells associated with the LUAD prognosis prediction model. Subsequently, we applied co-expression analysis to reveal the relationship between ferroptosis-related genes and significant immune cells.Results: The univariate COX regression analysis showed that 20 genes were associated with the overall survival (OS) as prognostic differentially expressed genes (DEGs) (FDR &lt;0.05). Patients were divided into two risk groups using a 13-gene signature, with the high-risk group having a significantly worse OS than their low-risk counterparts (p &lt; 0.001). We used receiver operating characteristic (ROC) curve analysis to confirm the predictive capacity of the signature. Besides, we identified seven pairs of ferroptosis-related genes and tumor-infiltrating immune cells associated with the prognosis of LUAD patients.Conclusion: In this study, we construct a ferroptosis-related gene signature that can be used for prognostic prediction in LUAD. In addition, we reveal a potential connection between ferroptosis and tumor-infiltrating immune cells.

scholarly journals Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

2020 ◽  
Vol 16 ◽  
pp. 117693432095157
Author(s):  
Jia Yan ◽  
Ming Shu ◽  
Xiang Li ◽  
Hua Yu ◽  
Shuhuai Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Liver Cancer ◽  
Cox Regression ◽  
Prognostic Score ◽  
Differentially Expressed ◽  
Clinical Factors ◽  
Cox Regression Analysis ◽  
Prognosis Prediction ◽  
Pathologic Stage

Hepatocellular carcinoma (HCC) is a common malignant tumor representing more than 90% of primary liver cancer. This study aimed to identify metabolism-related biomarkers with prognostic value by developing the novel prognostic score (PS) model. Transcriptomic profiles derived from TCGA and EBIArray databases were analyzed to identify differentially expressed genes (DEGs) in HCC tumor samples compared with normal samples. The overlapped genes between DEGs and metabolism-related genes (crucial genes) were screened and functionally analyzed. A novel PS model was constructed to identify optimal signature genes. Cox regression analysis was performed to identify independent clinical factors related to prognosis. Nomogram model was constructed to estimate the predictability of clinical factors. Finally, protein expression of crucial genes was explored in different cancer tissues and cell types from the Human Protein Atlas (HPA). We screened a total of 305 overlapped genes (differentially expressed metabolism-related genes). These genes were mainly involved in “oxidation reduction,” “steroid hormone biosynthesis,” “fatty acid metabolic process,” and “linoleic acid metabolism.” Furthermore, we screened ten optimal DEGs (CYP2C9, CYP3A4, and TKT, among others) by using the PS model. Two clinical factors of pathologic stage (P < .001, HR: 1.512 [1.219-1.875]) and PS status (P <.001, HR: 2.259 [1.522-3.354]) were independent prognostic predictors by cox regression analysis. Nomogram model showed a high predicted probability of overall survival time, and the AUC value was 0.837. The expression status of 7 proteins was frequently altered in normal or differential tumor tissues, such as liver cancer and stomach cancer samples.We have identified several metabolism-related biomarkers for prognosis prediction of HCC based on the PS model. Two clinical factors were independent prognostic predictors of pathologic stage and PS status (high/low risk). The prognosis prediction model described in this study is a useful and stable method for novel biomarker identification.

scholarly journals Systematic analysis and prediction model construction of alternative splicing events in hepatocellular carcinoma: a study on the basis of large-scale spliceseq data from The Cancer Genome Atlas

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8245 ◽  
Author(s):  
Lingpeng Yang ◽  
Yang He ◽  
Zifei Zhang ◽  
Wentao Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Gene Network ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Systematic Analysis ◽  
Cox Regression Analysis ◽  
Regulation Network ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Growing evidence showed that alternative splicing (AS) event is significantly related to tumor occurrence and progress. This study was performed to make a systematic analysis of AS events and constructed a robust prediction model of hepatocellular carcinoma (HCC). The clinical information and the genes expression profile data of 335 HCC patients were collected from The Cancer Genome Atlas (TCGA). Information of seven types AS events were collected from the TCGA SpliceSeq database. Overall survival (OS) related AS events and splicing factors (SFs) were identified using univariate Cox regression analysis. The corresponding genes of OS-related AS events were sent for gene network analysis and functional enrichment analysis. Optimal OS-related AS events were selected by LASSO regression to construct prediction model using multivariate Cox regression analysis. Prognostic value of the prediction models were assessed by receiver operating characteristic (ROC) curve and KaplanMeir survival analysis. The relationship between the Percent Spliced In (PSI) value of OS-related AS events and SFs expression were analyzed using Spearman correlation analysis. And the regulation network was generated by Cytoscape. A total of 34,163 AS events were identified, which consist of 3,482 OS-related AS events. UBB, UBE2D3, SF3A1 were the hub genes in the gene network of the top 800 OS-related AS events. The area under the curve (AUC) of the final prediction model based on seven types OS-related AS events was 0.878, 0.843, 0.821 in 1, 3, 5 years, respectively. Upon multivariate analysis, risk score (All) served as the risk factor to independently predict OS for HCC patients. SFs HNRNPH3 and HNRNPL were overexpressed in tumor samples and were signifcantly associated with the OS of HCC patients. The regulation network showed prominent correlation between the expression of SFs and OS-related AS events in HCC patients. The final prediction model performs well in predicting the prognosis of HCC patients. And the findings in this study improve our understanding of the association between AS events and HCC.

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